Joke Daems


2022

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Proceedings of the 23rd Annual Conference of the European Association for Machine Translation
Helena Moniz | Lieve Macken | Andrew Rufener | Loïc Barrault | Marta R. Costa-jussà | Christophe Declercq | Maarit Koponen | Ellie Kemp | Spyridon Pilos | Mikel L. Forcada | Carolina Scarton | Joachim Van den Bogaert | Joke Daems | Arda Tezcan | Bram Vanroy | Margot Fonteyne
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

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DeBiasByUs: Raising Awareness and Creating a Database of MT Bias
Joke Daems | Janiça Hackenbuchner
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

This paper presents the project initiated by the BiasByUs team resulting from the 2021 Artificially Correct Hackaton. We briefly explain our winning participation in the hackaton, tackling the challenge on ‘Database and detection of gender bi-as in A.I. translations’, we highlight the importance of gender bias in Machine Translation (MT), and describe our pro-posed solution to the challenge, the cur-rent status of the project, and our envi-sioned future collaborations and re-search.

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Writing in a second Language with Machine translation (WiLMa)
Margot Fonteyne | Maribel Montero Perez | Joke Daems | Lieve Macken
Proceedings of the 23rd Annual Conference of the European Association for Machine Translation

The WiLMa project aims to assess the effects of using machine translation (MT) tools on the writing processes of second language (L2) learners of varying proficiency. Particular attention is given to individual variation in learners’ tool use.

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GECO-MT: The Ghent Eye-tracking Corpus of Machine Translation
Toon Colman | Margot Fonteyne | Joke Daems | Nicolas Dirix | Lieve Macken
Proceedings of the Thirteenth Language Resources and Evaluation Conference

In the present paper, we describe a large corpus of eye movement data, collected during natural reading of a human translation and a machine translation of a full novel. This data set, called GECO-MT (Ghent Eye tracking Corpus of Machine Translation) expands upon an earlier corpus called GECO (Ghent Eye-tracking Corpus) by Cop et al. (2017). The eye movement data in GECO-MT will be used in future research to investigate the effect of machine translation on the reading process and the effects of various error types on reading. In this article, we describe in detail the materials and data collection procedure of GECO-MT. Extensive information on the language proficiency of our participants is given, as well as a comparison with the participants of the original GECO. We investigate the distribution of a selection of important eye movement variables and explore the possibilities for future analyses of the data. GECO-MT is freely available at https://www.lt3.ugent.be/resources/geco-mt.

2020

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Assessing the Comprehensibility of Automatic Translations (ArisToCAT)
Lieve Macken | Margot Fonteyne | Arda Tezcan | Joke Daems
Proceedings of the 22nd Annual Conference of the European Association for Machine Translation

The ArisToCAT project aims to assess the comprehensibility of ‘raw’ (unedited) MT output for readers who can only rely on the MT output. In this project description, we summarize the main results of the project and present future work.

2019

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When a ‘sport’ is a person and other issues for NMT of novels
Arda Tezcan | Joke Daems | Lieve Macken
Proceedings of the Qualities of Literary Machine Translation

2015

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The impact of machine translation error types on post-editing effort indicators
Joke Daems | Sonia Vandepitte | Robert Hartsuker | Lieve Macken
Proceedings of the 4th Workshop on Post-editing Technology and Practice

2014

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On the origin of errors: A fine-grained analysis of MT and PE errors and their relationship
Joke Daems | Lieve Macken | Sonia Vandepitte
Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC'14)

In order to improve the symbiosis between machine translation (MT) system and post-editor, it is not enough to know that the output of one system is better than the output of another system. A fine-grained error analysis is needed to provide information on the type and location of errors occurring in MT and the corresponding errors occurring after post-editing (PE). This article reports on a fine-grained translation quality assessment approach which was applied to machine translated-texts and the post-edited versions of these texts, made by student post-editors. By linking each error to the corresponding source text-passage, it is possible to identify passages that were problematic in MT, but not after PE, or passages that were problematic even after PE. This method provides rich data on the origin and impact of errors, which can be used to improve post-editor training as well as machine translation systems. We present the results of a pilot experiment on the post-editing of newspaper articles and highlight the advantages of our approach.

2013

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Quality as the sum of its parts: a two-step approach for the identification of translation problems and translation quality assessment for HT and MT+PE
Joke Daems | Lieve Macken | Sonia Vandepitte
Proceedings of the 2nd Workshop on Post-editing Technology and Practice